To maintain public order, a great number of road video systems are deployed. In the event of a public offense, investigators/police officers need to retrieve recorded data of associated location and time, and vast human resources and time are then spent to identify clues for solving the case. It is a common occurrence that much of the effort is spent in vain as the prime time for solving a case is often missed.
Therefore, computerized visual classification retrieval technologies for assisting manual search on a recording database are becoming popular. However, properties including resolutions, covered ranges and angles, and color deviations of video cameras of the deployed road video systems may be quite different. If a computerized visual classifier is incapable of automatically adapting to video data obtained from different image capturing properties, the system is unlikely to yield stable and reliable retrieval results.
To enhance classification performance of a computerized classification system under different scenes and lighting conditions, supervised adaptation algorithms and unsupervised adaptation algorithms have been proposed. In the supervised adaptation algorithms and unsupervised adaptation algorithms, parameters of a classifier are adjusted by image data collected under test environments, to adapt to image parameter distributions in the test environments.
The supervised adaptation algorithm, requiring manual labeling on image contents, has preferred performance but inadequate practicability due to high human resource costs. The unsupervised adaptation algorithm, although involving no manual labeling on image contents, suffers from possible hypothesis labeling errors. More particularly, the issue of hypothesis labeling errors can be aggravated when test environment data and training data are remarkably different, such that the unsupervised adaptation algorithm yields an even less satisfactory classification result.
A so-called semi-supervised adaptation algorithm that combines features of the two adaptive algorithms above is also available. The semi-supervised adaptation algorithm indeed offers more preferred and more stable performance, however still requires manually labeled image data when put to practice. Moreover, the semi-supervised adaptation algorithm also needs to further satisfy sample representativeness in order to correctly provide reference for non-manually-labeled image data.